Overview

Dataset statistics

Number of variables24
Number of observations13757
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory192.0 B

Variable types

Numeric7
Categorical17

Alerts

DT_GERACAO has constant value "12/04/2021" Constant
HH_GERACAO has constant value "14:31:33" Constant
ANO_ELEICAO has constant value "2018" Constant
SG_UF has constant value "ES" Constant
CD_MUNICIPIO has constant value "56030" Constant
NM_MUNICIPIO has constant value "ALEGRE" Constant
CD_MUN_SIT_BIOMETRICA has constant value "1" Constant
DS_MUN_SIT_BIOMETRICA has constant value "Biométrico" Constant
NR_ZONA has constant value "4" Constant
NR_SECAO is highly correlated with NR_LOCAL_VOTACAOHigh correlation
NR_LOCAL_VOTACAO is highly correlated with NR_SECAOHigh correlation
QT_ELEITORES_PERFIL is highly correlated with QT_ELEITORES_BIOMETRIAHigh correlation
QT_ELEITORES_BIOMETRIA is highly correlated with QT_ELEITORES_PERFILHigh correlation
NR_SECAO is highly correlated with NR_LOCAL_VOTACAOHigh correlation
NR_LOCAL_VOTACAO is highly correlated with NR_SECAOHigh correlation
QT_ELEITORES_PERFIL is highly correlated with QT_ELEITORES_BIOMETRIAHigh correlation
QT_ELEITORES_BIOMETRIA is highly correlated with QT_ELEITORES_PERFILHigh correlation
NR_SECAO is highly correlated with NR_LOCAL_VOTACAOHigh correlation
NR_LOCAL_VOTACAO is highly correlated with NR_SECAOHigh correlation
QT_ELEITORES_PERFIL is highly correlated with QT_ELEITORES_BIOMETRIAHigh correlation
QT_ELEITORES_BIOMETRIA is highly correlated with QT_ELEITORES_PERFILHigh correlation
DS_GENERO is highly correlated with NR_ZONA and 9 other fieldsHigh correlation
NR_ZONA is highly correlated with DS_GENERO and 15 other fieldsHigh correlation
HH_GERACAO is highly correlated with DS_GENERO and 15 other fieldsHigh correlation
CD_ESTADO_CIVIL is highly correlated with NR_ZONA and 9 other fieldsHigh correlation
QT_ELEITORES_DEFICIENCIA is highly correlated with NR_ZONA and 8 other fieldsHigh correlation
DS_MUN_SIT_BIOMETRICA is highly correlated with DS_GENERO and 15 other fieldsHigh correlation
DT_GERACAO is highly correlated with DS_GENERO and 15 other fieldsHigh correlation
QT_ELEITORES_INC_NM_SOCIAL is highly correlated with NR_ZONA and 8 other fieldsHigh correlation
DS_GRAU_ESCOLARIDADE is highly correlated with NR_ZONA and 8 other fieldsHigh correlation
DS_FAIXA_ETARIA is highly correlated with NR_ZONA and 8 other fieldsHigh correlation
NM_MUNICIPIO is highly correlated with DS_GENERO and 15 other fieldsHigh correlation
CD_MUNICIPIO is highly correlated with DS_GENERO and 15 other fieldsHigh correlation
CD_MUN_SIT_BIOMETRICA is highly correlated with DS_GENERO and 15 other fieldsHigh correlation
DS_ESTADO_CIVIL is highly correlated with NR_ZONA and 9 other fieldsHigh correlation
SG_UF is highly correlated with DS_GENERO and 15 other fieldsHigh correlation
ANO_ELEICAO is highly correlated with DS_GENERO and 15 other fieldsHigh correlation
CD_GENERO is highly correlated with DS_GENERO and 9 other fieldsHigh correlation
NR_SECAO is highly correlated with NR_LOCAL_VOTACAOHigh correlation
NR_LOCAL_VOTACAO is highly correlated with NR_SECAOHigh correlation
CD_GENERO is highly correlated with DS_GENEROHigh correlation
DS_GENERO is highly correlated with CD_GENEROHigh correlation
CD_ESTADO_CIVIL is highly correlated with DS_ESTADO_CIVIL and 2 other fieldsHigh correlation
DS_ESTADO_CIVIL is highly correlated with CD_ESTADO_CIVIL and 2 other fieldsHigh correlation
CD_FAIXA_ETARIA is highly correlated with CD_ESTADO_CIVIL and 2 other fieldsHigh correlation
DS_FAIXA_ETARIA is highly correlated with CD_ESTADO_CIVIL and 4 other fieldsHigh correlation
CD_GRAU_ESCOLARIDADE is highly correlated with DS_FAIXA_ETARIA and 1 other fieldsHigh correlation
DS_GRAU_ESCOLARIDADE is highly correlated with DS_FAIXA_ETARIA and 1 other fieldsHigh correlation
QT_ELEITORES_PERFIL is highly correlated with QT_ELEITORES_BIOMETRIAHigh correlation
QT_ELEITORES_BIOMETRIA is highly correlated with QT_ELEITORES_PERFILHigh correlation
df_index has unique values Unique

Reproduction

Analysis started2022-01-28 22:38:05.522989
Analysis finished2022-01-28 22:38:23.788827
Duration18.27 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct13757
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean700989.5415
Minimum535622
Maximum843630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.6 KiB
2022-01-28T19:38:23.934892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum535622
5-th percentile538380.8
Q1625276
median708244
Q3778743
95-th percentile841781.2
Maximum843630
Range308008
Interquartile range (IQR)153467

Descriptive statistics

Standard deviation94508.46658
Coefficient of variation (CV)0.1348215073
Kurtosis-1.053329303
Mean700989.5415
Median Absolute Deviation (MAD)80005
Skewness-0.2041224108
Sum9643513122
Variance8931850255
MonotonicityStrictly increasing
2022-01-28T19:38:24.097177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7884791
 
< 0.1%
6316171
 
< 0.1%
8405111
 
< 0.1%
7114861
 
< 0.1%
7901841
 
< 0.1%
7135291
 
< 0.1%
8430671
 
< 0.1%
7893001
 
< 0.1%
5394421
 
< 0.1%
6316011
 
< 0.1%
Other values (13747)13747
99.9%
ValueCountFrequency (%)
5356221
< 0.1%
5356231
< 0.1%
5356241
< 0.1%
5356251
< 0.1%
5356261
< 0.1%
5356271
< 0.1%
5356281
< 0.1%
5356291
< 0.1%
5356301
< 0.1%
5356311
< 0.1%
ValueCountFrequency (%)
8436301
< 0.1%
8436291
< 0.1%
8436281
< 0.1%
8436271
< 0.1%
8436261
< 0.1%
8436251
< 0.1%
8436241
< 0.1%
8436231
< 0.1%
8436221
< 0.1%
8436211
< 0.1%

DT_GERACAO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
12/04/2021
13757 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12/04/2021
2nd row12/04/2021
3rd row12/04/2021
4th row12/04/2021
5th row12/04/2021

Common Values

ValueCountFrequency (%)
12/04/202113757
100.0%

Length

2022-01-28T19:38:24.284664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:24.378395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
12/04/202113757
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HH_GERACAO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
14:31:33
13757 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14:31:33
2nd row14:31:33
3rd row14:31:33
4th row14:31:33
5th row14:31:33

Common Values

ValueCountFrequency (%)
14:31:3313757
100.0%

Length

2022-01-28T19:38:24.487709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:24.597058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
14:31:3313757
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ANO_ELEICAO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
2018
13757 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
201813757
100.0%

Length

2022-01-28T19:38:24.686279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:24.795597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
201813757
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SG_UF
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
ES
13757 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowES
2nd rowES
3rd rowES
4th rowES
5th rowES

Common Values

ValueCountFrequency (%)
ES13757
100.0%

Length

2022-01-28T19:38:24.889325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:24.998722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
es13757
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CD_MUNICIPIO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
56030
13757 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row56030
2nd row56030
3rd row56030
4th row56030
5th row56030

Common Values

ValueCountFrequency (%)
5603013757
100.0%

Length

2022-01-28T19:38:25.092438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:25.201753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
5603013757
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NM_MUNICIPIO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
ALEGRE
13757 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALEGRE
2nd rowALEGRE
3rd rowALEGRE
4th rowALEGRE
5th rowALEGRE

Common Values

ValueCountFrequency (%)
ALEGRE13757
100.0%

Length

2022-01-28T19:38:25.295480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:25.406509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
alegre13757
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CD_MUN_SIT_BIOMETRICA
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
1
13757 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
113757
100.0%

Length

2022-01-28T19:38:25.506497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:25.603314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
113757
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DS_MUN_SIT_BIOMETRICA
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
Biométrico
13757 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBiométrico
2nd rowBiométrico
3rd rowBiométrico
4th rowBiométrico
5th rowBiométrico

Common Values

ValueCountFrequency (%)
Biométrico13757
100.0%

Length

2022-01-28T19:38:25.701944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:25.795704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
biométrico13757
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NR_ZONA
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
4
13757 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
413757
100.0%

Length

2022-01-28T19:38:25.905056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:26.013834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
413757
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NR_SECAO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct98
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.84546049
Minimum1
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.6 KiB
2022-01-28T19:38:26.316743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q120
median42
Q382
95-th percentile120
Maximum158
Range157
Interquartile range (IQR)62

Descriptive statistics

Standard deviation38.30116217
Coefficient of variation (CV)0.7532857761
Kurtosis-0.3963880785
Mean50.84546049
Median Absolute Deviation (MAD)24
Skewness0.75270788
Sum699481
Variance1466.979024
MonotonicityNot monotonic
2022-01-28T19:38:26.711697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12204
 
1.5%
29201
 
1.5%
1201
 
1.5%
15200
 
1.5%
11195
 
1.4%
16194
 
1.4%
14194
 
1.4%
35194
 
1.4%
27193
 
1.4%
25191
 
1.4%
Other values (88)11790
85.7%
ValueCountFrequency (%)
1201
1.5%
2180
1.3%
3177
1.3%
4167
1.2%
5178
1.3%
6174
1.3%
7168
1.2%
8164
1.2%
9165
1.2%
10172
1.3%
ValueCountFrequency (%)
15837
 
0.3%
15765
 
0.5%
15668
 
0.5%
15573
 
0.5%
123151
1.1%
12275
0.5%
121150
1.1%
120183
1.3%
119128
0.9%
118113
0.8%

NR_LOCAL_VOTACAO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1151.137167
Minimum1015
Maximum1376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.6 KiB
2022-01-28T19:38:27.119600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1015
5-th percentile1023
Q11031
median1163
Q31210
95-th percentile1376
Maximum1376
Range361
Interquartile range (IQR)179

Descriptive statistics

Standard deviation114.4086545
Coefficient of variation (CV)0.09938750814
Kurtosis-0.7932655531
Mean1151.137167
Median Absolute Deviation (MAD)105
Skewness0.548624409
Sum15836194
Variance13089.34023
MonotonicityNot monotonic
2022-01-28T19:38:27.401707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
10231738
12.6%
10311617
11.8%
12101288
9.4%
11631248
 
9.1%
13761097
 
8.0%
1228839
 
6.1%
1198808
 
5.9%
1058789
 
5.7%
1040650
 
4.7%
1180506
 
3.7%
Other values (16)3177
23.1%
ValueCountFrequency (%)
1015201
 
1.5%
10231738
12.6%
10311617
11.8%
1040650
 
4.7%
1058789
5.7%
1066223
 
1.6%
1082276
 
2.0%
1090261
 
1.9%
1104343
 
2.5%
1120149
 
1.1%
ValueCountFrequency (%)
13761097
8.0%
1368113
 
0.8%
135065
 
0.5%
1341310
 
2.3%
1333120
 
0.9%
1325357
 
2.6%
130943
 
0.3%
1295120
 
0.9%
1228839
6.1%
12101288
9.4%

CD_GENERO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
4
7237 
2
6520 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
47237
52.6%
26520
47.4%

Length

2022-01-28T19:38:27.670825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:27.795796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
47237
52.6%
26520
47.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DS_GENERO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
FEMININO
7237 
MASCULINO
6520 

Length

Max length9
Median length8
Mean length8.473940539
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMASCULINO
2nd rowMASCULINO
3rd rowMASCULINO
4th rowMASCULINO
5th rowMASCULINO

Common Values

ValueCountFrequency (%)
FEMININO7237
52.6%
MASCULINO6520
47.4%

Length

2022-01-28T19:38:27.952010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:28.061400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
feminino7237
52.6%
masculino6520
47.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CD_ESTADO_CIVIL
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
3
5940 
1
5295 
9
1167 
5
1012 
7
 
343

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
35940
43.2%
15295
38.5%
91167
 
8.5%
51012
 
7.4%
7343
 
2.5%

Length

2022-01-28T19:38:28.170712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:28.280061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
35940
43.2%
15295
38.5%
91167
 
8.5%
51012
 
7.4%
7343
 
2.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DS_ESTADO_CIVIL
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
CASADO
5940 
SOLTEIRO
5295 
DIVORCIADO
1167 
VIÚVO
1012 
SEPARADO JUDICIALMENTE
 
343

Length

Max length22
Median length6
Mean length7.434469725
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSOLTEIRO
2nd rowSOLTEIRO
3rd rowSOLTEIRO
4th rowCASADO
5th rowCASADO

Common Values

ValueCountFrequency (%)
CASADO5940
43.2%
SOLTEIRO5295
38.5%
DIVORCIADO1167
 
8.5%
VIÚVO1012
 
7.4%
SEPARADO JUDICIALMENTE343
 
2.5%

Length

2022-01-28T19:38:28.483136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:28.592485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
casado5940
42.1%
solteiro5295
37.6%
divorciado1167
 
8.3%
viúvo1012
 
7.2%
judicialmente343
 
2.4%
separado343
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CD_FAIXA_ETARIA
Real number (ℝ)

HIGH CORRELATION

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4659.85084
Minimum-3
Maximum9999
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size107.6 KiB
2022-01-28T19:38:28.764508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile2000
Q13034
median4549
Q36064
95-th percentile7579
Maximum9999
Range10002
Interquartile range (IQR)3030

Descriptive statistics

Standard deviation1820.974502
Coefficient of variation (CV)0.3907795687
Kurtosis-0.774614192
Mean4659.85084
Median Absolute Deviation (MAD)1515
Skewness0.2775821145
Sum64105568
Variance3315948.135
MonotonicityNot monotonic
2022-01-28T19:38:28.920193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
50541253
9.1%
35391244
9.0%
40441198
8.7%
45491195
8.7%
30341182
8.6%
55591177
8.6%
60641130
8.2%
25291075
7.8%
6569900
 
6.5%
2124723
 
5.3%
Other values (13)2680
19.5%
ValueCountFrequency (%)
-32
 
< 0.1%
160045
 
0.3%
170077
 
0.6%
1800189
 
1.4%
1900241
 
1.8%
2000247
 
1.8%
2124723
5.3%
25291075
7.8%
30341182
8.6%
35391244
9.0%
ValueCountFrequency (%)
99997
 
0.1%
959921
 
0.2%
909485
 
0.6%
8589198
 
1.4%
8084344
 
2.5%
7579519
3.8%
7074705
5.1%
6569900
6.5%
60641130
8.2%
55591177
8.6%

DS_FAIXA_ETARIA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
50 a 54 anos
1253 
35 a 39 anos
1244 
40 a 44 anos
1198 
45 a 49 anos
1195 
30 a 34 anos
1182 
Other values (18)
7685 

Length

Max length30
Median length30
Mean length30
Min length30

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25 a 29 anos
2nd row30 a 34 anos
3rd row55 a 59 anos
4th row45 a 49 anos
5th row50 a 54 anos

Common Values

ValueCountFrequency (%)
50 a 54 anos 1253
9.1%
35 a 39 anos 1244
9.0%
40 a 44 anos 1198
8.7%
45 a 49 anos 1195
8.7%
30 a 34 anos 1182
8.6%
55 a 59 anos 1177
8.6%
60 a 64 anos 1130
8.2%
25 a 29 anos 1075
7.8%
65 a 69 anos 900
 
6.5%
21 a 24 anos 723
 
5.3%
Other values (13)2680
19.5%

Length

2022-01-28T19:38:29.084018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
anos13755
25.7%
a12949
24.2%
501253
 
2.3%
541253
 
2.3%
351244
 
2.3%
391244
 
2.3%
401198
 
2.2%
441198
 
2.2%
451195
 
2.2%
491195
 
2.2%
Other values (33)16940
31.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CD_GRAU_ESCOLARIDADE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.462237406
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.6 KiB
2022-01-28T19:38:29.206426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.155715442
Coefficient of variation (CV)0.4831019163
Kurtosis-1.065210637
Mean4.462237406
Median Absolute Deviation (MAD)2
Skewness0.1136104004
Sum61387
Variance4.647109068
MonotonicityNot monotonic
2022-01-28T19:38:29.331398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
33471
25.2%
62635
19.2%
81749
12.7%
51454
10.6%
11412
10.3%
41309
 
9.5%
2967
 
7.0%
7760
 
5.5%
ValueCountFrequency (%)
11412
10.3%
2967
 
7.0%
33471
25.2%
41309
 
9.5%
51454
10.6%
62635
19.2%
7760
 
5.5%
81749
12.7%
ValueCountFrequency (%)
81749
12.7%
7760
 
5.5%
62635
19.2%
51454
10.6%
41309
 
9.5%
33471
25.2%
2967
 
7.0%
11412
10.3%

DS_GRAU_ESCOLARIDADE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
ENSINO FUNDAMENTAL INCOMPLETO
3471 
ENSINO MÉDIO COMPLETO
2635 
SUPERIOR COMPLETO
1749 
ENSINO MÉDIO INCOMPLETO
1454 
ANALFABETO
1412 
Other values (3)
3036 

Length

Max length29
Median length21
Mean length21.42007705
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENSINO FUNDAMENTAL COMPLETO
2nd rowENSINO FUNDAMENTAL INCOMPLETO
3rd rowENSINO FUNDAMENTAL COMPLETO
4th rowLÊ E ESCREVE
5th rowENSINO MÉDIO COMPLETO

Common Values

ValueCountFrequency (%)
ENSINO FUNDAMENTAL INCOMPLETO3471
25.2%
ENSINO MÉDIO COMPLETO2635
19.2%
SUPERIOR COMPLETO1749
12.7%
ENSINO MÉDIO INCOMPLETO1454
10.6%
ANALFABETO1412
10.3%
ENSINO FUNDAMENTAL COMPLETO1309
 
9.5%
LÊ E ESCREVE967
 
7.0%
SUPERIOR INCOMPLETO760
 
5.5%

Length

2022-01-28T19:38:29.523234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:29.639564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
ensino8869
24.7%
completo5693
15.8%
incompleto5685
15.8%
fundamental4780
13.3%
médio4089
11.4%
superior2509
 
7.0%
analfabeto1412
 
3.9%
escreve967
 
2.7%
e967
 
2.7%
967
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

QT_ELEITORES_PERFIL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.68132587
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.6 KiB
2022-01-28T19:38:29.842642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum26
Range25
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.371545798
Coefficient of variation (CV)0.8157525093
Kurtosis29.36362813
Mean1.68132587
Median Absolute Deviation (MAD)0
Skewness4.009163621
Sum23130
Variance1.881137876
MonotonicityNot monotonic
2022-01-28T19:38:29.998857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
19081
66.0%
22565
 
18.6%
3990
 
7.2%
4531
 
3.9%
5262
 
1.9%
6137
 
1.0%
780
 
0.6%
835
 
0.3%
932
 
0.2%
1012
 
0.1%
Other values (10)32
 
0.2%
ValueCountFrequency (%)
19081
66.0%
22565
 
18.6%
3990
 
7.2%
4531
 
3.9%
5262
 
1.9%
6137
 
1.0%
780
 
0.6%
835
 
0.3%
932
 
0.2%
1012
 
0.1%
ValueCountFrequency (%)
261
 
< 0.1%
211
 
< 0.1%
192
 
< 0.1%
181
 
< 0.1%
172
 
< 0.1%
153
 
< 0.1%
142
 
< 0.1%
136
< 0.1%
123
 
< 0.1%
1111
0.1%

QT_ELEITORES_BIOMETRIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.676092171
Minimum0
Maximum26
Zeros46
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size107.6 KiB
2022-01-28T19:38:30.139449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum26
Range26
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.372227616
Coefficient of variation (CV)0.8187065363
Kurtosis29.2157202
Mean1.676092171
Median Absolute Deviation (MAD)0
Skewness3.988772619
Sum23058
Variance1.88300863
MonotonicityNot monotonic
2022-01-28T19:38:30.283315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
19046
65.8%
22559
 
18.6%
3990
 
7.2%
4526
 
3.8%
5264
 
1.9%
6136
 
1.0%
779
 
0.6%
046
 
0.3%
836
 
0.3%
931
 
0.2%
Other values (11)44
 
0.3%
ValueCountFrequency (%)
046
 
0.3%
19046
65.8%
22559
 
18.6%
3990
 
7.2%
4526
 
3.8%
5264
 
1.9%
6136
 
1.0%
779
 
0.6%
836
 
0.3%
931
 
0.2%
ValueCountFrequency (%)
261
 
< 0.1%
211
 
< 0.1%
192
 
< 0.1%
181
 
< 0.1%
172
 
< 0.1%
153
 
< 0.1%
142
 
< 0.1%
135
< 0.1%
124
 
< 0.1%
1111
0.1%

QT_ELEITORES_DEFICIENCIA
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
0
13642 
1
 
115

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
013642
99.2%
1115
 
0.8%

Length

2022-01-28T19:38:30.564553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:30.665010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
013642
99.2%
1115
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

QT_ELEITORES_INC_NM_SOCIAL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
0
13753 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
013753
> 99.9%
14
 
< 0.1%

Length

2022-01-28T19:38:30.774363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T19:38:30.868127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
013753
> 99.9%
14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-01-28T19:38:20.875824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:09.630484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:11.589593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:13.574375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:15.683378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:17.800242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:19.309695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:21.067407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:09.973751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:11.806016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:13.937325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:16.070342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:18.064532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:19.507184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:21.288323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:10.251011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:12.096239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:14.250209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:16.497202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:18.293271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:19.727037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:21.622688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:10.611046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:12.575957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:14.535445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:16.753516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:18.510780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:19.962701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:21.808381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:10.832655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:12.948960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:14.736905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:16.973927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:18.720311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:20.193790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:22.005171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:11.032115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:13.168801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:14.958354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:17.260162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:18.928061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:20.445071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:22.185560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:11.279423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:13.373363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:15.373206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:17.486558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:19.108774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-28T19:38:20.685146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-01-28T19:38:30.993061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-28T19:38:31.413727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-28T19:38:31.811445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-28T19:38:32.229667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-28T19:38:32.656721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-28T19:38:22.547867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-28T19:38:23.505932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexDT_GERACAOHH_GERACAOANO_ELEICAOSG_UFCD_MUNICIPIONM_MUNICIPIOCD_MUN_SIT_BIOMETRICADS_MUN_SIT_BIOMETRICANR_ZONANR_SECAONR_LOCAL_VOTACAOCD_GENERODS_GENEROCD_ESTADO_CIVILDS_ESTADO_CIVILCD_FAIXA_ETARIADS_FAIXA_ETARIACD_GRAU_ESCOLARIDADEDS_GRAU_ESCOLARIDADEQT_ELEITORES_PERFILQT_ELEITORES_BIOMETRIAQT_ELEITORES_DEFICIENCIAQT_ELEITORES_INC_NM_SOCIAL
053562212/04/202114:31:332018ES56030ALEGRE1Biométrico4110152MASCULINO1SOLTEIRO252925 a 29 anos4ENSINO FUNDAMENTAL COMPLETO1100
153562312/04/202114:31:332018ES56030ALEGRE1Biométrico4110152MASCULINO1SOLTEIRO303430 a 34 anos3ENSINO FUNDAMENTAL INCOMPLETO1100
253562412/04/202114:31:332018ES56030ALEGRE1Biométrico4110152MASCULINO1SOLTEIRO555955 a 59 anos4ENSINO FUNDAMENTAL COMPLETO2200
353562512/04/202114:31:332018ES56030ALEGRE1Biométrico4110152MASCULINO3CASADO454945 a 49 anos2LÊ E ESCREVE1100
453562612/04/202114:31:332018ES56030ALEGRE1Biométrico4110152MASCULINO3CASADO505450 a 54 anos6ENSINO MÉDIO COMPLETO3300
553562712/04/202114:31:332018ES56030ALEGRE1Biométrico4110152MASCULINO3CASADO505450 a 54 anos8SUPERIOR COMPLETO3300
653562812/04/202114:31:332018ES56030ALEGRE1Biométrico4110152MASCULINO3CASADO757975 a 79 anos2LÊ E ESCREVE1100
753562912/04/202114:31:332018ES56030ALEGRE1Biométrico4110152MASCULINO5VIÚVO707470 a 74 anos3ENSINO FUNDAMENTAL INCOMPLETO1100
853563012/04/202114:31:332018ES56030ALEGRE1Biométrico4110152MASCULINO5VIÚVO808480 a 84 anos1ANALFABETO2110
953563112/04/202114:31:332018ES56030ALEGRE1Biométrico4110152MASCULINO5VIÚVO909490 a 94 anos3ENSINO FUNDAMENTAL INCOMPLETO1100

Last rows

df_indexDT_GERACAOHH_GERACAOANO_ELEICAOSG_UFCD_MUNICIPIONM_MUNICIPIOCD_MUN_SIT_BIOMETRICADS_MUN_SIT_BIOMETRICANR_ZONANR_SECAONR_LOCAL_VOTACAOCD_GENERODS_GENEROCD_ESTADO_CIVILDS_ESTADO_CIVILCD_FAIXA_ETARIADS_FAIXA_ETARIACD_GRAU_ESCOLARIDADEDS_GRAU_ESCOLARIDADEQT_ELEITORES_PERFILQT_ELEITORES_BIOMETRIAQT_ELEITORES_DEFICIENCIAQT_ELEITORES_INC_NM_SOCIAL
1374784362112/04/202114:31:332018ES56030ALEGRE1Biométrico415710232MASCULINO3CASADO303430 a 34 anos8SUPERIOR COMPLETO1100
1374884362212/04/202114:31:332018ES56030ALEGRE1Biométrico415710232MASCULINO3CASADO555955 a 59 anos3ENSINO FUNDAMENTAL INCOMPLETO1100
1374984362312/04/202114:31:332018ES56030ALEGRE1Biométrico415710234FEMININO1SOLTEIRO212421 a 24 anos5ENSINO MÉDIO INCOMPLETO2200
1375084362412/04/202114:31:332018ES56030ALEGRE1Biométrico415710234FEMININO1SOLTEIRO353935 a 39 anos3ENSINO FUNDAMENTAL INCOMPLETO1100
1375184362512/04/202114:31:332018ES56030ALEGRE1Biométrico415710234FEMININO1SOLTEIRO404440 a 44 anos5ENSINO MÉDIO INCOMPLETO1100
1375284362612/04/202114:31:332018ES56030ALEGRE1Biométrico415710234FEMININO1SOLTEIRO505450 a 54 anos8SUPERIOR COMPLETO1100
1375384362712/04/202114:31:332018ES56030ALEGRE1Biométrico415710234FEMININO9DIVORCIADO505450 a 54 anos3ENSINO FUNDAMENTAL INCOMPLETO1100
1375484362812/04/202114:31:332018ES56030ALEGRE1Biométrico415710234FEMININO9DIVORCIADO555955 a 59 anos2LÊ E ESCREVE1100
1375584362912/04/202114:31:332018ES56030ALEGRE1Biométrico415811632MASCULINO1SOLTEIRO190019 anos5ENSINO MÉDIO INCOMPLETO4400
1375684363012/04/202114:31:332018ES56030ALEGRE1Biométrico415811632MASCULINO1SOLTEIRO190019 anos6ENSINO MÉDIO COMPLETO2200